Evidence map›Paper›PMID 41228219›Full record

ReviewCancers2025

Urinary Biomarkers in Bladder Cancer: FDA-Approved Tests and Emerging Tools for Diagnosis and Surveillance.

Zhenyun Yang, Fengyu Song, Jin Zhong

Abstract readReview
In one paragraph

Review in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Review
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  7. Integrative in silico and clinical validation of urinary lncRNAs (SNHG7 & LINC00675) and β-catenin mRNA as molecular markers for early bladder cancer detection.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Zhenyun YangDepartment of Internal Medicine, School of Medicine, University of California, Riverside, CA 92521, USA.
Fengyu SongDepartment of Internal Medicine, School of Medicine, University of California, Riverside, CA 92521, USA.
Jin ZhongDepartment of Internal Medicine, School of Medicine, University of California, Riverside, CA 92521, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bladder cancer is a prevalent malignancy with high morbidity and mortality, particularly when diagnosed at an advanced stage. Early detection is critical, as it significantly improves prognosis and the patient's outcomes. Bladder cancer also has a high recurrence rate, necessitating long-term surveillance. While cystoscopy remains the gold standard for diagnosis and monitoring, it is invasive and costly. Urine cytology, though widely used, has high specificity for detecting high-grade urothelial carcinoma but suffers from low sensitivity and limited effectiveness as a stand-alone diagnostic tool. Urinary biomarkers offer a promising, noninvasive alternative for early detection and disease surveillance. This review examines FDA-approved urinary biomarker tests, including NMP 22, UroVysion, and BTA, highlighting their clinical utility and limitations. Additionally, we explore emerging biomarkers such as DNA methylation assays, genomic alterations, and proteomic signatures as well as advanced technologies like next-generation sequencing and machine learning-based platforms. These innovations have the potential to enhance diagnostic accuracy, risk stratification, and recurrent monitoring, ultimately improving early detection and long-term disease management. By evaluating both established and emerging urinary biomarkers, this review aims to provide clinicians and researchers with insights into evolving tools for bladder cancer diagnosis and surveillance.

Indexed as

artificial intelligencebladder cancernext-generation sequencingurinary biomarkersurine cytology

Identifiers

PMID41228219
PMCPMC12607409

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.